Reachable Tube

Reachable tube analysis aims to mathematically define the set of all possible future states of a dynamic system, crucial for verifying the safety of autonomous systems like robots and vehicles. Current research focuses on improving the accuracy and efficiency of computing these tubes, particularly for high-dimensional systems, using neural networks and algorithms like DeepReach, often coupled with verification methods such as scenario optimization and conformal prediction to provide probabilistic safety guarantees. This work is significant because it enables the formal verification of complex autonomous systems, addressing critical safety concerns and paving the way for wider deployment of these technologies in safety-critical applications.